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A Fast Real-time Facial Expression Classifier Deep Learning-based for Human-robot Interaction
Muhamad Dwisnanto Putro,Duy-Linh Nguyen,Kang-Hyun Jo 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
Human-robot interaction drives the need for vision technology to recognize user expressions. Convolutional Neural Networks (CNN) has been introduced as a robust facial feature extractor and can overcome classification task. However, it is not supported by efficient computation for real-time applications. The work proposes an efficient CNN architecture to recognize human facial expressions that consist of five stages containing a combination of lightweight convolution operations. It introduces the efficient contextual extractor with a partial transfer module to suppress computational compression. This technique is applied to the mid and high-level features by separating the channel-based input features into two parts. Then it applies sequential convolution to only one part and combines it with the previous separated part. A shuffle channel group is used to exchange the information extracted. The structure of the entire network generates less than a million parameters. The CK+ and KDEF datasets are used as training and test sets to evaluate the performance of the proposed architecture. As a result, the proposed classifier obtains an accuracy that is competitive with other methods. In addition, the efficiency of the classifier has strongly suitable for implementation to edge devices by achieving 43 FPS on a Jetson Nano.
Fast Eye Detector Using CPU Based Lightweight Convolutional Neural Network
Muhamad Dwisnanto Putro,Duy-Linh Nguyen,Kang-Hyun Jo 제어로봇시스템학회 2020 제어로봇시스템학회 국제학술대회 논문집 Vol.2020 No.10
Eye detection is a crucial task for the success of driver drowsiness detection. The eye location is determined by employing feature extractions to discriminate distinctive features. Convolutional Neural Networks (CNN) has achieved the best results in the object detection task. However, this requires expensive computation, whereas practical application demands for this work to run real-time on low-cost devices such as CPU. The eye feature is very different from other organ facial features, so the shallow architecture of CNN deserves to be employed. This paper proposes the Fast Eye-CPU (FE-CPU) as a real-time eye detector that can work on the CPU. The architecture consists of two main modules, including the backbone to rapidly extract features and the detection module for predicting eye regions. As a result, the detector achieves high accuracy on several benchmark datasets, and it can work in real-time by 467 frames per second on Intel I5-6600 as a CPU device.
A Facial Gender Detector on CPU using Multi-dilated Convolution with Attention Modules
Adri Priadana,Muhamad Dwisnanto Putro,Xuan-Thuy Vo,Kang-Hyun Jo 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
Facial gender detectors have evolved into a vital component of an intelligent advertisement display platform. It is helpful to assist a decision of delivering appropriate advertisements to each audience. To reduce system costs, applications deployed in this platform must be able to run on a CPU. This work proposes a facial gender detector (FGCPU) that can be implemented on a CPU device to support an advertising display platform. The proposed CNN model consists of a multi-dilated convolution with attention modules (MudaNet). The multi-dilated convolution is applied to capture multi-scale features in an efficient manner. The attention module is used to rectify the quality of the feature map. This work’s training and validation process is conducted on the UTKFace, the Labeled Faces in the Wild (LFW), and the Adience Benchmark datasets. As a result, the proposed CNN model is proven to compete with other common and lightweight competitors’ CNN models on these three datasets. Regarding speed, the detector can operate 49.19 frames per second in real-time on a CPU device.
Hand Detector based on Efficient and Lighweight Convolutional Neural Network
Duy-Linh Nguyen,Muhamad Dwisnanto Putro,Kang-Hyun Jo 제어로봇시스템학회 2020 제어로봇시스템학회 국제학술대회 논문집 Vol.2020 No.10
Hand detection and recognition topic has been studied since the last century and is particularly concerned with the development of machine learning today. Inspired by the benefits of a convolution neural network (CNN), this paper proposed an efficient and lightweight architecture to detect the location of hand in the images. This network is deployed with two main blocks which are the feature extraction and the detection block. The feature extraction block starts by convolution layers, CReLU (Concatenated Rectified Linear Unit) module, and max pooling layers alternately. After that, the six inception modules are used and final by four convolution layers. The detection block is constructed by three blocks of two-sibling convolution layers using for classification and regression. The experiment was trained on the combination of EgoHands and Hand dataset. As evaluation, the detector was tested on Egohands test dataset with the results achieved 93.32% of AP (Average Precision). In addition, the speed was tested in real-time by 33.87 fps (frames per second) on Intel Core I7-4770 CPU @ 3.40 ㎓.
Duy-Linh Nguyen,Muhamad Dwisnanto Putro,Kang-Hyun Jo 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
The traffic accident is a big problem in the world and it is happening every day. One of the main causes is distracted driving. Those are the actions of the driver when they are not focusing on driving on the road such as using the cellphone, drinking, makeup, talking to others, etc. For driver warning purpose, this paper proposes a distracted driver recognizer with a simple and efficient Convolutional Neural Network (CNN). The evaluation results on the State Farm Distracted Driver Detection dataset with ten activities achieved an accuracy of 99.51% and on video with the latency allowed for deployment in the real-time system based on a low-computation device.